Ai Risk Management Framework
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AI Risk Management Framework Concept Paper
This concept paper describes the fundamental approach proposed for the National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework (AI RMF or framework). The AI RMF is intended for voluntary use and to address risks in the design, development, use, and evaluation of AI products, services, and systems. AI risk management follows similar processes as other disciplines. Nevertheless, managing AI risks presents unique challenges. A voluntary consensus-driven framework can help create and safeguard trust at the heart of AI-driven systems and business models and permit the flexibility for innovation, allowing the framework to develop along with the technology.
AI Risk Management Framework
This initial draft of the Artificial Intelligence Risk Management Framework (AI RMF, or Framework) builds on the concept paper released in December 2021 and incorporates the feedback received. The AI RMF is intended for voluntary use in addressing risks in the design, development, use, and evaluation of AI products, services, and systems. Part I of the AI RMF sets the stage for why the AI RMF is important and explains its intended use and audience. Part II includes the AI RMF Core and Profiles. Part III includes a companion Practice Guide to assist in adopting the AI RMF.
AI Risk Management, Analysis, and Assessment.
This book provides a comprehensive exploration of AI risk management, addressing foundational concepts, advanced analysis methodologies, assessment frameworks, governance models, industry-specific applications, and future challenges. Beginning with the fundamentals, it clarifies key definitions and classifications of AI risks, differentiates risk from uncertainty, and examines historical lessons. It categorizes risks across technical, ethical, economic, and environmental dimensions, emphasizing the evolving lifecycle of AI risk from design through deployment and continuous monitoring. The discussion advances into rigorous risk analysis techniques, combining quantitative and qualitative approaches such as probabilistic risk assessment, scenario simulation, and bias audits. AI-specific modeling techniques including causal networks, Monte Carlo simulations, and agent-based models are explored, highlighting tools to detect and mitigate bias and fairness issues while improving explainability. Frameworks and standards like NIST AI RMF, ISO/IEC guidelines, and OECD principles provide structured approaches to risk assessment, while operational practices and toolkits integrate risk considerations directly into AI development pipelines. Governance sections detail internal structures, accountability mechanisms, and legal challenges including cross-border compliance, data protection, and liability. Third-party and supply chain risks emphasize the complexity of AI ecosystems. Industry-focused chapters explore sector-specific risks in healthcare, finance, and defense, illustrating practical applications and regulatory requirements. Finally, the book addresses emerging risks from generative AI, autonomous agents, and AI-enhanced cyber threats, as well as the profound challenges posed by AGI. It advocates for resilience engineering, human-centered design, and multi-stakeholder governance to build trustworthy AI and ensure responsible innovation in an uncertain future.